title: Model-based, goal-oriented, individualised drug therapy. Linkage of population modelling, new 'multiple model' dosage design, bayesian feedback and individualised target goals.
abstract: This article examines the use of population pharmacokinetic models to store experiences about drugs in patients and to apply that experience to the care of new patients. Population models are the Bayesian prior. For truly individualised therapy, it is necessary first to select a specific target goal, such as a desired serum or peripheral compartment concentration, and then to develop the dosage regimen individualised to best hit that target in that patient. One must monitor the behaviour of the drug by measuring serum concentrations or other responses, hopefully obtained at optimally chosen times, not only to see the raw results, but to also make an individualised (Bayesian posterior) model of how the drug is behaving in that patient. Only then can one see the relationship between the dose and the absorption, distribution, effect and elimination of the drug, and the patient's clinical sensitivity to it; one must always look at the patient. Only by looking at both the patient and the model can it be judged whether the target goal was correct or needs to be changed. The adjusted dosage regimen is again developed to hit that target most precisely starting with the very next dose, not just for some future steady state. Nonparametric population models have discrete, not continuous, parameter distributions. These lead naturally into the multiple model method of dosage design, specifically to hit a desired target with the greatest possible precision for whatever past experience and present data are available on that drug--a new feature for this goal-oriented, model-based, individualised drug therapy. As clinical versions of this new approach become available from several centers, it should lead to further improvements in patient care, especially for bacterial and viral infections, cardiovascular therapy, and cancer and transplant situations.
authors.affiliation: Laboratory of Applied Pharmacokinetics, University of Southern California School of Medicine, Los Angeles, USA.
authors.forename: R W
authors.initials: RW
authors.lastname: Jelliffe
authors.forename: A
authors.initials: A
authors.lastname: Schumitzky
authors.forename: D
authors.initials: D
authors.lastname: Bayard
authors.forename: M
authors.initials: M
authors.lastname: Milman
authors.forename: M
authors.initials: M
authors.lastname: Van Guilder
authors.forename: X
authors.initials: X
authors.lastname: Wang
authors.forename: F
authors.initials: F
authors.lastname: Jiang
authors.forename: X
authors.initials: X
authors.lastname: Barbaut
authors.forename: P
authors.initials: P
authors.lastname: Maire
mesh_terms: D000889:Anti-Arrhythmia Agents / Q000008:administration & dosage / Q000627:therapeutic use; D000900:Anti-Bacterial Agents / Q000008:administration & dosage / Q000627:therapeutic use; D001499:Bayes Theorem; D004077:Digoxin / Q000008:administration & dosage / Q000627:therapeutic use; D016903:Drug Monitoring; D004358:Drug Therapy / Q000379:methods*; D006801:Humans; D008954:Models, Biological; D010599:Pharmacokinetics; D018709:Statistics, Nonparametric; D013810:Therapeutic Equivalency; D014640:Vancomycin / Q000008:administration & dosage / Q000627:therapeutic use
chemical_list: D000889:Anti-Arrhythmia Agents; D000900:Anti-Bacterial Agents; D014640:Vancomycin; D004077:Digoxin